Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are implicated in the regulation of tumor cell ferroptosis. However, the prognostic value of ferroptosis-related lncRNAs has never been comprehensively explored in glioma. In this study, the transcriptomic data and clinical information of glioma patients were downloaded from TCGA, CGGA and Rembrandt databases. We identified 24 prognostic ferroptosis-related lncRNAs, 15 of which (SNAI3-AS1, GDNF-AS1, WDFY3-AS2, CPB2-AS1, WAC-AS1, SLC25A21-AS1, ARHGEF26-AS1, LINC00641, LINC00844, MIR155HG, MIR22HG, PVT1, SNHG18, PAXIP1-AS2, and SBF2-AS1) were used to construct a ferroptosis-related lncRNAs signature (FRLS) according to the least absolute shrinkage and selection operator (LASSO) regression. The validity of this FRLS was verified in training (TCGA) and validation (CGGA and Rembrandt) cohorts, respectively. The Kaplan-Meier curves revealed a significant distinction of overall survival (OS) between the high- and low-risk groups. The receiver operating characteristic (ROC) curves exhibited robust prognostic capacity of this FRLS. A nomogram with improved accuracy for predicting OS was established based on independent prognostic factors (FRLS, age, and WHO grade). Besides, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, and higher expression of immune checkpoints. Patients in the low-risk group benefited significantly from radiotherapy, while no survival benefit of radiotherapy was observed for those in the high-risk group. In conclusion, we identified the prognostic ferroptosis-related lncRNAs in glioma, and constructed a prognostic signature which was associated with the immune landscape of glioma microenvironment and radiotherapy response.

As an inflammatory form of programmed cell death, pyroptosis has been well established to be associated with tumorigenesis and tumor immune microenvironment. In this paper, we aimed at the construction of a pyroptosis-related gene prognostic index (PRGPI) for predicting prognosis and guiding individualized immunotherapy in glioma patients. Patients and Methods: Pyroptosis-related genes (PRGs) were identified based on a detailed review of published literatures. The transcriptome data and clinical information of glioma patients were obtained from CGGA and TCGA databases. PRGPI was constructed by using the multivariate Cox regression method. The immune cell infiltration level was analyzed via CIBERSORT algorithm. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to evaluate the potential response to immune checkpoint inhibitor (ICI) therapy. The expression patterns of PRGs in PRGPI were validated in cell lines and pathological specimens. Results: We identified a total of 31 PRGs. Among them, PRGs (CASP3, DPP9, MAPK8, PELP1 and TOMM20) were selected for the construction of PRGPI. In both training (CGGA693) and validation (CGGA325 and TCGA) cohorts, PRGPI-high patients showed an inferior survival outcome compared with PRGPI-low patients. ROC curves illustrated that the prognostic prediction power of PRGPI was robust. A nomogram was developed based on independent prognostic indicators (PRGPI, age and WHO grade), and also exhibited a strong forecasting ability for overall survival (OS). Additionally, PRGPI-high patients exhibited higher immune, stroma and ESTIMATE scores, lower tumor purity, higher infiltration of M2type macrophages, lower infiltration of CD8 + T cells and activated NK cells, higher tumor mutation burden (TMB), and higher expression of immune checkpoints. TIDE showed that PRGPI-high group had more responders of ICI therapy than PRGPI-low group. Finally, the expression patterns of five selected PRGs in PRGPI were significantly different between normal and glioma. Conclusion:The constructed PRGPI can be used for predicting prognosis and guiding individualized immunotherapy in glioma patients.

Increasing evidences have revealed that N6-methyladenosine (m6A) RNA methylation regulators participate in the tumorigenesis and development of multiple tumors. So far, there has been little comprehension about the effects of m6A RNA methylation regulators on lower-grade gliomas (LGG). Here, we systematically investigated the expression profiles and prognostic significance of 36 m6A RNA methylation regulators in LGG patients from the TCGA and CGGA databases. Most of the m6A RNA methylation regulators are differentially expressed in LGG tissues as compared with normal brain tissues and glioblastoma (GBM) tissues. The consensus clustering for these m6A RNA methylation regulators identified three clusters. Patients in cluster 3 exhibited worse prognosis. In addition, we constructed an m6A-related prognostic signature, which exhibited excellent performance in prognostic stratification of LGG patients according to the results of the Kaplan-Meier curves, ROC curves, and univariate and multivariate Cox regression analyses. In addition, a significant correlation was observed between the m6A-related prognostic signature and the immune landscape of the LGG microenvironment. The high-risk group exhibited higher immune scores, stromal scores, and ESTIMATE scores but lower tumor purity and lower abundance of activated NK cells. Moreover, the expression level of immune checkpoints was positively correlated with the risk score. To conclude, the current research systematically demonstrated the prognostic roles of m6A RNA methylation regulators in LGG.